• Journal of Biosystems Engineering
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• 제31권1호
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• pp.52-58
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• 2006

Various robots were developed for harvesting fruits and vegetables. However, each robot was designed for a specific task such as harvesting apples or vegetables. This has been a big hurdle in application of robots to agriculture. A new type of hybrid manipulator with both parallel and serial joints was developed and designed to apply to various kinds of field operations. The hybrid manipulator had 2 extra degree of freedom in serial joints which made it flexible in switching one to the other type of hybrid manipulator, for example, PUMA to SCARA. And it was designed to harvest heavy fruits such as musky melons or water melons even behind leaves or branches of tree. This hybrid manipulator showed less than $\pm1mm$ position error. It was concluded that the hybrid manipulator was an effective and feasible tool to perform various works and to increase working performance.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 하계 학술대회 논문집
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• pp.251-256
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• 2000

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 하계 학술대회 논문집
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• pp.257-262
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• 2000

• Journal of Biosystems Engineering
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• 제25권2호
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• pp.141-150
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• 2000

This study was carried out to de develop a control strategy of a fruit harvesting redundant robot. The method of generating a safe trajectory, which avoids collisions with obstracles such as branches or immature fruits, in the 3D(3-dimension) space using artificial potential field technique and virtual plane concept was proposed. Also, the method of setting reference velocity vectors to follow the trajectory and to avoid obstacles in the 3D space was proposed. Developed methods were verified with computer simulations and with actual robot tests. Fro the actual robot tests, a machine vision system was used for detecting fruits and obstacles, Results showed that developed control method could reduce the occurrences of the robot manipulator located in the possible collision distance. with 10 virtual obstacles generated randomly in the 3 D space, maximum rates of the occurrences of the robot manipulator located in the possible collision distance, 0.03 m, from the obstacles were 8 % with 5 degree of freedom (DOF), 8 % with 6-DOF, and 4% with 7-DOF, respectively.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 동계 학술대회 논문집
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• pp.587-593
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• 2000

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 동계 학술대회 논문집
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• pp.594-600
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• 2000

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1998년도 동계 학술대회 논문집
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• pp.475-480
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• 1998

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1998년도 동계 학술대회 논문집
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• pp.481-486
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• 1998

• Journal of Biosystems Engineering
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• 제29권3호
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• pp.281-286
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• 2004

The end-effector is the one of the important factors on development of the cucumber robot to harvester a cucumber. Three end-effectors were designed the single blade end-effector with one blade, the double blade end-effector with two blades and the triple blade end-effector with three blades. Performance tests of the end-effector, the fully integrated system, were conducted to determine the cutting rate by using two different kinds of cucumber. The success rates of cucumber cutting ratio of single end-effector, double end-effector and triple end-effector in laboratory. were 61.7%, 95%, 86.7%, respectively. The cutting rate of single blade or double blade was a little difference with respect to the different diameters of cucumber stem. However, the success cutting rate of the end-effector with triple blade was 61.7% under 29mm diameter of a grabbing stem section. The triple end-effector was not suitable for harvesting a cucumber, but was considered to be suitable for harvesting a grape, an apple and a tomato. The success rate of cucumber cutting ratio of triple end-effectors in greenhouse was 84%. The failure cutting rate was 16% which are due to abnormal shape of cucumber fruit.

• 농업과학연구
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• 제49권4호
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• pp.785-793
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• 2022

In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.